The present invention is related to the field of image processing, and more particularly to techniques for post-processing of decoded images to remove undesirable artifacts such as ringing and blocking.
Image compression is useful for efficient electronic storage of images as well as efficient transmission of images over a communications medium, such as the Internet. Many image compression techniques are in use today. The generalized image compression scheme employs a cascade of functional elements. For example, a transform may be applied to the original image to convert the image information to a more convenient domain, such as the frequency domain. The transformed image information is quantized to discrete levels that can be represented by a finite digital word, and the quantized result is compressed according to a compression coding algorithm. At the receiver, these functions are performed in reverse order to recover a facsimile of the original image.
Many compression techniques introduce certain types of noise, referred to as "artifacts", into the image. For example, an algorithm known as Joint Picture Expert Group (JPEG) encoding tends to introduce blocking artifacts at medium and low bit-rates. Blocking artifacts appear as subtle rectangular segmentation in the decoded image. In the JPEG algorithm, blocking arises from the use of short and non-overlapping basis functions.
Blocking can be reduced or eliminated using other transforms, such as wavelet transforms, that have overlapping basis functions. However, these may introduce spurious oscillations in the vicinity of major edges at low bit-rates. Such coding artifacts are called ringing artifacts. In a wavelet encoding scheme, ringing artifacts are caused by the abrupt truncation of the high frequency wavelet coefficients. It is desirable to obtain an image that is as free of compression-related artifacts as possible, thus improving image quality at low bit rates.
An artifact-free image can be estimated from the compressed image by maximum a posteriori (MAP) estimation techniques. The problem is to generate an artifact-free estimate f' of an original image f given a compressed image g. In MAP estimation approaches, the estimate f' is considered to be a random variable whose properties are modeled by a probability density, following the Bayesian viewpoint. The MAP estimate is the estimate f' that maximizes the posterior probability that is expressed in terms of a conditional probability and a prior probability. For transform-based coders-decoders (codecs), the conditional probability is modeled in the transform domain, while the prior probability is modeled in the spatial domain. This aspect significantly increases the computational complexity when the solution is obtained through iterative algorithms.
In addition to MAP techniques, there are other techniques that can be used to reduce artifacts, such as an algorithm known as Projection Onto Convex Sets (POCS). However, algorithms such as MAP and POCS suffer from the disadvantage of requiring the use of both the forward and the inverse transforms. Also, these algorithms are iterative in nature, so the forward and the inverse transforms are needed at each of several iterations of the algorithm. This aspect of existing algorithms increases their computational complexity significantly.
It would be desirable to reduce artifacts appearing in decoded compressed images while reducing the extensiveness and complexity of the required computational resources.